{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/tensorflow/tensorrt/blob/r2.0/tftrt/examples/image_classification/TFv2-TF-TRT-inference-from-Keras-saved-model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dR1W9kv7IPhE"
   },
   "outputs": [],
   "source": [
    "# Copyright 2019 NVIDIA Corporation. All Rights Reserved.\n",
    "#\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License.\n",
    "# =============================================================================="
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Yb3TdMZAkVNq"
   },
   "source": [
    "<img src=\"http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png\" style=\"width: 90px; float: right;\">\n",
    "\n",
    "# TF-TRT Inference from Keras Model with TensorFlow 2.0\n",
    "\n",
    "\n",
    "## Introduction\n",
    "The NVIDIA TensorRT is a C++ library that facilitates high performance inference on NVIDIA graphics processing units (GPUs). TensorRT takes a trained network, which consists of a network definition and a set of trained parameters, and produces a highly optimized runtime engine which performs inference for that network. \n",
    "\n",
    "TensorFlow™ integration with TensorRT™ (TF-TRT) optimizes and executes compatible subgraphs, allowing TensorFlow to execute the remaining graph. While you can still use TensorFlow's wide and flexible feature set, TensorRT will parse the model and apply optimizations to the portions of the graph wherever possible.\n",
    "\n",
    "In this notebook, we demonstrate the process of creating a TF-TRT optimized model from a ResNet-50 Keras saved model.\n",
    "\n",
    "## Requirement\n",
    "\n",
    "### GPU\n",
    "\n",
    "This demo will work on any NVIDIA GPU with CUDA cores, though for improved FP16 and INT8 inference, a Volta, Turing or newer generation GPU with Tensor cores is desired."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8Fg4x4aomCY4",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LG4IBNn-2PWY"
   },
   "source": [
    "### Install Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "!pip install pillow matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "v0mfnfqg3ned",
    "outputId": "11c043a0-b8e5-49e2-f907-5f1372c92a68",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "print(\"Tensorflow version: \", tf.version.VERSION)\n",
    "\n",
    "# check TensorRT version\n",
    "print(\"TensorRT version: \")\n",
    "!dpkg -l | grep nvinfer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9U8b2394CZRu"
   },
   "source": [
    "A successfull TensorRT installation looks like:\n",
    "\n",
    "```\n",
    "TensorRT version: \n",
    "ii  libnvinfer5   5.1.5-1+cuda10.1   amd64        TensorRT runtime libraries\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SeekwaXj4CbI"
   },
   "source": [
    "### Check Tensor core GPU\n",
    "The below code check whether a Tensor-core GPU is present."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "VHLld1VNkVOa",
    "outputId": "a0d2b13c-fe62-476c-9589-190bdcfff233"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor Core GPU Present: True\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.python.client import device_lib\n",
    "\n",
    "def check_tensor_core_gpu_present():\n",
    "    local_device_protos = device_lib.list_local_devices()\n",
    "    for line in local_device_protos:\n",
    "        if \"compute capability\" in str(line):\n",
    "            compute_capability = float(line.physical_device_desc.split(\"compute capability: \")[-1])\n",
    "            if compute_capability>=7.0:\n",
    "                return True\n",
    "    \n",
    "print(\"Tensor Core GPU Present:\", check_tensor_core_gpu_present())\n",
    "tensor_core_gpu = check_tensor_core_gpu_present()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nWYufTjPCMgW"
   },
   "source": [
    "### Importing required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Yyzwxjlm37jx"
   },
   "outputs": [],
   "source": [
    "from __future__ import absolute_import, division, print_function, unicode_literals\n",
    "import os\n",
    "import time\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.python.compiler.tensorrt import trt_convert as trt\n",
    "from tensorflow.python.saved_model import tag_constants\n",
    "from tensorflow.keras.applications.resnet50 import ResNet50\n",
    "from tensorflow.keras.preprocessing import image\n",
    "from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "v-R2iN4akVOi"
   },
   "source": [
    "## Data\n",
    "We download several random images for testing from the Internet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tVJ2-8rokVOl",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "!mkdir ./data\n",
    "!wget  -O ./data/img0.JPG \"https://d17fnq9dkz9hgj.cloudfront.net/breed-uploads/2018/08/siberian-husky-detail.jpg?bust=1535566590&width=630\"\n",
    "!wget  -O ./data/img1.JPG \"https://www.hakaimagazine.com/wp-content/uploads/header-gulf-birds.jpg\"\n",
    "!wget  -O ./data/img2.JPG \"https://www.artis.nl/media/filer_public_thumbnails/filer_public/00/f1/00f1b6db-fbed-4fef-9ab0-84e944ff11f8/chimpansee_amber_r_1920x1080.jpg__1920x1080_q85_subject_location-923%2C365_subsampling-2.jpg\"\n",
    "!wget  -O ./data/img3.JPG \"https://www.familyhandyman.com/wp-content/uploads/2018/09/How-to-Avoid-Snakes-Slithering-Up-Your-Toilet-shutterstock_780480850.jpg\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 269
    },
    "colab_type": "code",
    "id": "F_9n-AR1kVOv",
    "outputId": "e0ead6dc-e761-404e-a030-f6d3057a57da"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tensorflow.keras.preprocessing import image\n",
    "\n",
    "fig, axes = plt.subplots(nrows=2, ncols=2)\n",
    "\n",
    "for i in range(4):\n",
    "  img_path = './data/img%d.JPG'%i\n",
    "  img = image.load_img(img_path, target_size=(224, 224))\n",
    "  plt.subplot(2,2,i+1)\n",
    "  plt.imshow(img);\n",
    "  plt.axis('off');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xeV4r2YTkVO1"
   },
   "source": [
    "## Model\n",
    "\n",
    "We next download and test a ResNet-50 pre-trained model from the Keras model zoo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 73
    },
    "colab_type": "code",
    "id": "WwRBOikEkVO3",
    "outputId": "2d63bc46-8bac-492f-b519-9ae5f19176bc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels.h5\n",
      "102973440/102967424 [==============================] - 1s 0us/step\n"
     ]
    }
   ],
   "source": [
    "model = ResNet50(weights='imagenet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 410
    },
    "colab_type": "code",
    "id": "lFKQPoLO_ikd",
    "outputId": "c0b93de8-c94b-4977-992e-c780e12a3d52"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/download.tensorflow.org/data/imagenet_class_index.json\n",
      "40960/35363 [==================================] - 0s 0us/step\n",
      "./data/img0.JPG - Predicted: [('n02110185', 'Siberian_husky', 0.55662036), ('n02109961', 'Eskimo_dog', 0.4173731), ('n02110063', 'malamute', 0.020951612)]\n",
      "./data/img1.JPG - Predicted: [('n01820546', 'lorikeet', 0.30138972), ('n01537544', 'indigo_bunting', 0.16979563), ('n01828970', 'bee_eater', 0.16134152)]\n",
      "./data/img2.JPG - Predicted: [('n02481823', 'chimpanzee', 0.5149863), ('n02480495', 'orangutan', 0.15896699), ('n02480855', 'gorilla', 0.15318178)]\n",
      "./data/img3.JPG - Predicted: [('n01729977', 'green_snake', 0.4377243), ('n03627232', 'knot', 0.08848081), ('n01749939', 'green_mamba', 0.08080045)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(4):\n",
    "  img_path = './data/img%d.JPG'%i\n",
    "  img = image.load_img(img_path, target_size=(224, 224))\n",
    "  x = image.img_to_array(img)\n",
    "  x = np.expand_dims(x, axis=0)\n",
    "  x = preprocess_input(x)\n",
    "\n",
    "  preds = model.predict(x)\n",
    "  # decode the results into a list of tuples (class, description, probability)\n",
    "  # (one such list for each sample in the batch)\n",
    "  print('{} - Predicted: {}'.format(img_path, decode_predictions(preds, top=3)[0]))\n",
    "\n",
    "  plt.subplot(2,2,i+1)\n",
    "  plt.imshow(img);\n",
    "  plt.axis('off');\n",
    "  plt.title(decode_predictions(preds, top=3)[0][0][1])\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XrL3FEcdkVPA"
   },
   "source": [
    "TF-TRT takes input as aTensorFlow saved model, therefore, we re-export the Keras model as a TF saved model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 110
    },
    "colab_type": "code",
    "id": "WxlUF3rlkVPH",
    "outputId": "9f3864e7-f211-4c06-d2d2-585c1a477e34"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/tracking.py:111: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/tracking.py:111: Layer.updates (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
      "INFO:tensorflow:Assets written to: resnet50_saved_model/assets\n"
     ]
    }
   ],
   "source": [
    "# Save the entire model as a SavedModel.\n",
    "model.save('resnet50_saved_model') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 453
    },
    "colab_type": "code",
    "id": "RBu2RKs6kVPP",
    "outputId": "8e063261-7efb-47fd-fa6c-1bb5076d418c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-10-19 22:14:08.490797: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n",
      "\n",
      "MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:\n",
      "\n",
      "signature_def['__saved_model_init_op']:\n",
      "  The given SavedModel SignatureDef contains the following input(s):\n",
      "  The given SavedModel SignatureDef contains the following output(s):\n",
      "    outputs['__saved_model_init_op'] tensor_info:\n",
      "        dtype: DT_INVALID\n",
      "        shape: unknown_rank\n",
      "        name: NoOp\n",
      "  Method name is: \n",
      "\n",
      "signature_def['serving_default']:\n",
      "  The given SavedModel SignatureDef contains the following input(s):\n",
      "    inputs['input_1'] tensor_info:\n",
      "        dtype: DT_FLOAT\n",
      "        shape: (-1, 224, 224, 3)\n",
      "        name: serving_default_input_1:0\n",
      "  The given SavedModel SignatureDef contains the following output(s):\n",
      "    outputs['predictions'] tensor_info:\n",
      "        dtype: DT_FLOAT\n",
      "        shape: (-1, 1000)\n",
      "        name: StatefulPartitionedCall:0\n",
      "  Method name is: tensorflow/serving/predict\n",
      "\n",
      "Defined Functions:\n",
      "  Function Name: '__call__'\n",
      "    Option #1\n",
      "      Callable with:\n",
      "        Argument #1\n",
      "          inputs: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='inputs')\n",
      "        Argument #2\n",
      "          DType: bool\n",
      "          Value: False\n",
      "        Argument #3\n",
      "          DType: NoneType\n",
      "          Value: None\n",
      "    Option #2\n",
      "      Callable with:\n",
      "        Argument #1\n",
      "          input_1: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='input_1')\n",
      "        Argument #2\n",
      "          DType: bool\n",
      "          Value: False\n",
      "        Argument #3\n",
      "          DType: NoneType\n",
      "          Value: None\n",
      "    Option #3\n",
      "      Callable with:\n",
      "        Argument #1\n",
      "          inputs: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='inputs')\n",
      "        Argument #2\n",
      "          DType: bool\n",
      "          Value: True\n",
      "        Argument #3\n",
      "          DType: NoneType\n",
      "          Value: None\n",
      "    Option #4\n",
      "      Callable with:\n",
      "        Argument #1\n",
      "          input_1: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='input_1')\n",
      "        Argument #2\n",
      "          DType: bool\n",
      "          Value: True\n",
      "        Argument #3\n",
      "          DType: NoneType\n",
      "          Value: None\n",
      "\n",
      "  Function Name: '_default_save_signature'\n",
      "    Option #1\n",
      "      Callable with:\n",
      "        Argument #1\n",
      "          input_1: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='input_1')\n",
      "\n",
      "  Function Name: 'call_and_return_all_conditional_losses'\n",
      "    Option #1\n",
      "      Callable with:\n",
      "        Argument #1\n",
      "          inputs: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='inputs')\n",
      "        Argument #2\n",
      "          DType: bool\n",
      "          Value: False\n",
      "        Argument #3\n",
      "          DType: NoneType\n",
      "          Value: None\n",
      "    Option #2\n",
      "      Callable with:\n",
      "        Argument #1\n",
      "          input_1: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='input_1')\n",
      "        Argument #2\n",
      "          DType: bool\n",
      "          Value: False\n",
      "        Argument #3\n",
      "          DType: NoneType\n",
      "          Value: None\n",
      "    Option #3\n",
      "      Callable with:\n",
      "        Argument #1\n",
      "          inputs: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='inputs')\n",
      "        Argument #2\n",
      "          DType: bool\n",
      "          Value: True\n",
      "        Argument #3\n",
      "          DType: NoneType\n",
      "          Value: None\n",
      "    Option #4\n",
      "      Callable with:\n",
      "        Argument #1\n",
      "          input_1: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='input_1')\n",
      "        Argument #2\n",
      "          DType: bool\n",
      "          Value: True\n",
      "        Argument #3\n",
      "          DType: NoneType\n",
      "          Value: None\n"
     ]
    }
   ],
   "source": [
    "!saved_model_cli show --all --dir resnet50_saved_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qBQwBvlNm-J8"
   },
   "source": [
    "### Inference with native TF2.0 saved model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8zLN0GMCkVPe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
     ]
    }
   ],
   "source": [
    "model = tf.keras.models.load_model('resnet50_saved_model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 219
    },
    "colab_type": "code",
    "id": "Fbj-UEOxkVPs",
    "outputId": "3a2b34f9-8034-48cb-b3fe-477f09966025"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./data/img0.JPG - Predicted: [('n02110185', 'Siberian_husky', 0.55662036), ('n02109961', 'Eskimo_dog', 0.4173731), ('n02110063', 'malamute', 0.020951612)]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Siberian_husky')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img_path = './data/img0.JPG'  # Siberian_husky\n",
    "img = image.load_img(img_path, target_size=(224, 224))\n",
    "x = image.img_to_array(img)\n",
    "x = np.expand_dims(x, axis=0)\n",
    "x = preprocess_input(x)\n",
    "\n",
    "preds = model.predict(x)\n",
    "# decode the results into a list of tuples (class, description, probability)\n",
    "# (one such list for each sample in the batch)\n",
    "print('{} - Predicted: {}'.format(img_path, decode_predictions(preds, top=3)[0]))\n",
    "plt.subplot(2,2,1)\n",
    "plt.imshow(img);\n",
    "plt.axis('off');\n",
    "plt.title(decode_predictions(preds, top=3)[0][0][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "CGc-dC6DvwRP",
    "outputId": "e0a22e05-f4fe-47b6-93e8-2b806bf7098a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "batched_input shape:  (8, 224, 224, 3)\n"
     ]
    }
   ],
   "source": [
    "batch_size = 8\n",
    "batched_input = np.zeros((batch_size, 224, 224, 3), dtype=np.float32)\n",
    "\n",
    "for i in range(batch_size):\n",
    "  img_path = './data/img%d.JPG' % (i % 4)\n",
    "  img = image.load_img(img_path, target_size=(224, 224))\n",
    "  x = image.img_to_array(img)\n",
    "  x = np.expand_dims(x, axis=0)\n",
    "  x = preprocess_input(x)\n",
    "  batched_input[i, :] = x\n",
    "batched_input = tf.constant(batched_input)\n",
    "print('batched_input shape: ', batched_input.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rFBV6hQR7N3z"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 0: 58.4ms\n",
      "Step 50: 61.4ms\n",
      "Step 100: 62.1ms\n",
      "Step 150: 62.8ms\n",
      "Step 200: 62.0ms\n",
      "Step 250: 63.0ms\n",
      "Step 300: 62.0ms\n",
      "Step 350: 62.2ms\n",
      "Step 400: 62.7ms\n",
      "Step 450: 62.4ms\n",
      "Step 500: 62.3ms\n",
      "Step 550: 62.1ms\n",
      "Step 600: 62.0ms\n",
      "Step 650: 62.5ms\n",
      "Step 700: 62.1ms\n",
      "Step 750: 62.4ms\n",
      "Step 800: 61.5ms\n",
      "Step 850: 66.7ms\n",
      "Step 900: 61.4ms\n",
      "Step 950: 62.6ms\n",
      "Throughput: 128 images/s\n"
     ]
    }
   ],
   "source": [
    "# Benchmarking throughput\n",
    "N_warmup_run = 50\n",
    "N_run = 1000\n",
    "elapsed_time = []\n",
    "\n",
    "for i in range(N_warmup_run):\n",
    "  preds = model.predict(batched_input)\n",
    "\n",
    "for i in range(N_run):\n",
    "  start_time = time.time()\n",
    "  preds = model.predict(batched_input)\n",
    "  end_time = time.time()\n",
    "  elapsed_time = np.append(elapsed_time, end_time - start_time)\n",
    "  if i % 50 == 0:\n",
    "    print('Step {}: {:4.1f}ms'.format(i, (elapsed_time[-50:].mean()) * 1000))\n",
    "\n",
    "print('Throughput: {:.0f} images/s'.format(N_run * batch_size / elapsed_time.sum()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vC_RN0BAkVPy"
   },
   "source": [
    "### TF-TRT FP32 model\n",
    "\n",
    "We first convert the TF native FP32 model to a TF-TRT FP32 model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 126
    },
    "colab_type": "code",
    "id": "0eLImSJ-kVPz",
    "outputId": "e2c353c7-8e4b-49aa-ab97-f4d82797d4d8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converting to TF-TRT FP32...\n",
      "INFO:tensorflow:Linked TensorRT version: (7, 1, 3)\n",
      "INFO:tensorflow:Loaded TensorRT version: (7, 1, 3)\n",
      "INFO:tensorflow:Could not find TRTEngineOp_0_0 in TF-TRT cache. This can happen if build() is not called, which means TensorRT engines will be built and cached at runtime.\n",
      "INFO:tensorflow:Assets written to: resnet50_saved_model_TFTRT_FP32/assets\n",
      "Done Converting to TF-TRT FP32\n"
     ]
    }
   ],
   "source": [
    "print('Converting to TF-TRT FP32...')\n",
    "conversion_params = trt.DEFAULT_TRT_CONVERSION_PARAMS._replace(precision_mode=trt.TrtPrecisionMode.FP32,\n",
    "                                                               max_workspace_size_bytes=8000000000)\n",
    "\n",
    "converter = trt.TrtGraphConverterV2(input_saved_model_dir='resnet50_saved_model',\n",
    "                                    conversion_params=conversion_params)\n",
    "converter.convert()\n",
    "converter.save(output_saved_model_dir='resnet50_saved_model_TFTRT_FP32')\n",
    "print('Done Converting to TF-TRT FP32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 453
    },
    "colab_type": "code",
    "id": "dlue_3npkVQC",
    "outputId": "4dd6a366-fe9a-43c8-aad0-dd357bba41bb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-10-19 22:16:15.817450: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n",
      "\n",
      "MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:\n",
      "\n",
      "signature_def['__saved_model_init_op']:\n",
      "  The given SavedModel SignatureDef contains the following input(s):\n",
      "  The given SavedModel SignatureDef contains the following output(s):\n",
      "    outputs['__saved_model_init_op'] tensor_info:\n",
      "        dtype: DT_INVALID\n",
      "        shape: unknown_rank\n",
      "        name: NoOp\n",
      "  Method name is: \n",
      "\n",
      "signature_def['serving_default']:\n",
      "  The given SavedModel SignatureDef contains the following input(s):\n",
      "    inputs['input_1'] tensor_info:\n",
      "        dtype: DT_FLOAT\n",
      "        shape: (-1, 224, 224, 3)\n",
      "        name: serving_default_input_1:0\n",
      "  The given SavedModel SignatureDef contains the following output(s):\n",
      "    outputs['predictions'] tensor_info:\n",
      "        dtype: DT_FLOAT\n",
      "        shape: unknown_rank\n",
      "        name: PartitionedCall:0\n",
      "  Method name is: tensorflow/serving/predict\n",
      "\n",
      "Defined Functions:\n",
      "  Function Name: '__call__'\n",
      "    Option #1\n",
      "      Callable with:\n",
      "        Argument #1\n",
      "          inputs: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='inputs')\n",
      "        Argument #2\n",
      "          DType: bool\n",
      "          Value: False\n",
      "        Argument #3\n",
      "          DType: NoneType\n",
      "          Value: None\n",
      "    Option #2\n",
      "      Callable with:\n",
      "        Argument #1\n",
      "          input_1: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='input_1')\n",
      "        Argument #2\n",
      "          DType: bool\n",
      "          Value: False\n",
      "        Argument #3\n",
      "          DType: NoneType\n",
      "          Value: None\n",
      "    Option #3\n",
      "      Callable with:\n",
      "        Argument #1\n",
      "          inputs: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='inputs')\n",
      "        Argument #2\n",
      "          DType: bool\n",
      "          Value: True\n",
      "        Argument #3\n",
      "          DType: NoneType\n",
      "          Value: None\n",
      "    Option #4\n",
      "      Callable with:\n",
      "        Argument #1\n",
      "          input_1: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='input_1')\n",
      "        Argument #2\n",
      "          DType: bool\n",
      "          Value: True\n",
      "        Argument #3\n",
      "          DType: NoneType\n",
      "          Value: None\n",
      "\n",
      "  Function Name: '_default_save_signature'\n",
      "    Option #1\n",
      "      Callable with:\n",
      "        Argument #1\n",
      "          input_1: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='input_1')\n",
      "\n",
      "  Function Name: 'call_and_return_all_conditional_losses'\n",
      "    Option #1\n",
      "      Callable with:\n",
      "        Argument #1\n",
      "          inputs: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='inputs')\n",
      "        Argument #2\n",
      "          DType: bool\n",
      "          Value: False\n",
      "        Argument #3\n",
      "          DType: NoneType\n",
      "          Value: None\n",
      "    Option #2\n",
      "      Callable with:\n",
      "        Argument #1\n",
      "          input_1: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='input_1')\n",
      "        Argument #2\n",
      "          DType: bool\n",
      "          Value: False\n",
      "        Argument #3\n",
      "          DType: NoneType\n",
      "          Value: None\n",
      "    Option #3\n",
      "      Callable with:\n",
      "        Argument #1\n",
      "          input_1: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='input_1')\n",
      "        Argument #2\n",
      "          DType: bool\n",
      "          Value: True\n",
      "        Argument #3\n",
      "          DType: NoneType\n",
      "          Value: None\n",
      "    Option #4\n",
      "      Callable with:\n",
      "        Argument #1\n",
      "          inputs: TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='inputs')\n",
      "        Argument #2\n",
      "          DType: bool\n",
      "          Value: True\n",
      "        Argument #3\n",
      "          DType: NoneType\n",
      "          Value: None\n"
     ]
    }
   ],
   "source": [
    "!saved_model_cli show --all --dir resnet50_saved_model_TFTRT_FP32"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Vd2DoGUp8ivj"
   },
   "source": [
    "Next, we load and test the TF-TRT FP32 model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rf97K_rxvwRm"
   },
   "outputs": [],
   "source": [
    "def predict_tftrt(input_saved_model):\n",
    "    \"\"\"Runs prediction on a single image and shows the result.\n",
    "    input_saved_model (string): Name of the input model stored in the current dir\n",
    "    \"\"\"\n",
    "    img_path = './data/img0.JPG'  # Siberian_husky\n",
    "    img = image.load_img(img_path, target_size=(224, 224))\n",
    "    x = image.img_to_array(img)\n",
    "    x = np.expand_dims(x, axis=0)\n",
    "    x = preprocess_input(x)\n",
    "    x = tf.constant(x)\n",
    "    \n",
    "    saved_model_loaded = tf.saved_model.load(input_saved_model, tags=[tag_constants.SERVING])\n",
    "    signature_keys = list(saved_model_loaded.signatures.keys())\n",
    "    print(signature_keys)\n",
    "\n",
    "    infer = saved_model_loaded.signatures['serving_default']\n",
    "    print(infer.structured_outputs)\n",
    "\n",
    "    labeling = infer(x)\n",
    "    preds = labeling['predictions'].numpy()\n",
    "    print('{} - Predicted: {}'.format(img_path, decode_predictions(preds, top=3)[0]))\n",
    "    plt.subplot(2,2,1)\n",
    "    plt.imshow(img);\n",
    "    plt.axis('off');\n",
    "    plt.title(decode_predictions(preds, top=3)[0][0][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 238
    },
    "colab_type": "code",
    "id": "pRK0pRE-snvb",
    "outputId": "1f7ab6c1-dbfa-4e3e-a21d-df9975c70455"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['serving_default']\n",
      "{'predictions': TensorSpec(shape=<unknown>, dtype=tf.float32, name='predictions')}\n",
      "./data/img0.JPG - Predicted: [('n02110185', 'Siberian_husky', 0.5566208), ('n02109961', 'Eskimo_dog', 0.4173726), ('n02110063', 'malamute', 0.020951547)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict_tftrt('resnet50_saved_model_TFTRT_FP32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "z9b5j6jMvwRt"
   },
   "outputs": [],
   "source": [
    "def benchmark_tftrt(input_saved_model):\n",
    "    saved_model_loaded = tf.saved_model.load(input_saved_model, tags=[tag_constants.SERVING])\n",
    "    infer = saved_model_loaded.signatures['serving_default']\n",
    "\n",
    "    N_warmup_run = 50\n",
    "    N_run = 1000\n",
    "    elapsed_time = []\n",
    "\n",
    "    for i in range(N_warmup_run):\n",
    "      labeling = infer(batched_input)\n",
    "\n",
    "    for i in range(N_run):\n",
    "      start_time = time.time()\n",
    "      labeling = infer(batched_input)\n",
    "      end_time = time.time()\n",
    "      elapsed_time = np.append(elapsed_time, end_time - start_time)\n",
    "      if i % 50 == 0:\n",
    "        print('Step {}: {:4.1f}ms'.format(i, (elapsed_time[-50:].mean()) * 1000))\n",
    "\n",
    "    print('Throughput: {:.0f} images/s'.format(N_run * batch_size / elapsed_time.sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ai6bxNcNszHc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 0: 11.1ms\n",
      "Step 50: 11.2ms\n",
      "Step 100: 11.2ms\n",
      "Step 150: 11.2ms\n",
      "Step 200: 11.2ms\n",
      "Step 250: 11.2ms\n",
      "Step 300: 11.2ms\n",
      "Step 350: 11.2ms\n",
      "Step 400: 11.3ms\n",
      "Step 450: 11.3ms\n",
      "Step 500: 11.3ms\n",
      "Step 550: 11.3ms\n",
      "Step 600: 11.4ms\n",
      "Step 650: 11.3ms\n",
      "Step 700: 11.4ms\n",
      "Step 750: 11.4ms\n",
      "Step 800: 11.4ms\n",
      "Step 850: 11.3ms\n",
      "Step 900: 11.4ms\n",
      "Step 950: 11.4ms\n",
      "Throughput: 708 images/s\n"
     ]
    }
   ],
   "source": [
    "benchmark_tftrt('resnet50_saved_model_TFTRT_FP32')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "G2F8t6cPkVQS"
   },
   "source": [
    "### TF-TRT FP16 model\n",
    "We next convert the native TF FP32 saved model to TF-TRT FP16 model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 126
    },
    "colab_type": "code",
    "id": "0ia_AlSDkVQT",
    "outputId": "d29eb6de-101b-4b9a-8ebf-4880a2e469bf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converting to TF-TRT FP16...\n",
      "INFO:tensorflow:Linked TensorRT version: (7, 1, 3)\n",
      "INFO:tensorflow:Loaded TensorRT version: (7, 1, 3)\n",
      "INFO:tensorflow:Could not find TRTEngineOp_1_0 in TF-TRT cache. This can happen if build() is not called, which means TensorRT engines will be built and cached at runtime.\n",
      "INFO:tensorflow:Assets written to: resnet50_saved_model_TFTRT_FP16/assets\n",
      "Done Converting to TF-TRT FP16\n"
     ]
    }
   ],
   "source": [
    "print('Converting to TF-TRT FP16...')\n",
    "conversion_params = trt.DEFAULT_TRT_CONVERSION_PARAMS._replace(\n",
    "    precision_mode=trt.TrtPrecisionMode.FP16,\n",
    "    max_workspace_size_bytes=8000000000)\n",
    "converter = trt.TrtGraphConverterV2(\n",
    "   input_saved_model_dir='resnet50_saved_model', conversion_params=conversion_params)\n",
    "converter.convert()\n",
    "converter.save(output_saved_model_dir='resnet50_saved_model_TFTRT_FP16')\n",
    "print('Done Converting to TF-TRT FP16')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 238
    },
    "colab_type": "code",
    "id": "yTDbB6DWn0kJ",
    "outputId": "33cd23f8-9c8b-4cdd-9c7a-4939aae12f56"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['serving_default']\n",
      "{'predictions': TensorSpec(shape=<unknown>, dtype=tf.float32, name='predictions')}\n",
      "./data/img0.JPG - Predicted: [('n02110185', 'Siberian_husky', 0.54345703), ('n02109961', 'Eskimo_dog', 0.42993164), ('n02110063', 'malamute', 0.021255493)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict_tftrt('resnet50_saved_model_TFTRT_FP16')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "G_gerN5j9l6U"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 0:  4.0ms\n",
      "Step 50:  4.1ms\n",
      "Step 100:  4.2ms\n",
      "Step 150:  4.2ms\n",
      "Step 200:  4.1ms\n",
      "Step 250:  4.2ms\n",
      "Step 300:  4.2ms\n",
      "Step 350:  4.2ms\n",
      "Step 400:  4.2ms\n",
      "Step 450:  4.2ms\n",
      "Step 500:  4.1ms\n",
      "Step 550:  4.2ms\n",
      "Step 600:  4.2ms\n",
      "Step 650:  4.2ms\n",
      "Step 700:  4.1ms\n",
      "Step 750:  4.2ms\n",
      "Step 800:  4.2ms\n",
      "Step 850:  4.2ms\n",
      "Step 900:  4.2ms\n",
      "Step 950:  4.2ms\n",
      "Throughput: 1919 images/s\n"
     ]
    }
   ],
   "source": [
    "benchmark_tftrt('resnet50_saved_model_TFTRT_FP16')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qKSJ-oizkVQY"
   },
   "source": [
    "### TF-TRT INT8 model\n",
    "\n",
    "Creating TF-TRT INT8 model requires a small calibration dataset. This data set ideally should represent the test data in production well, and will be used to create a value histogram for each layer in the neural network for effective 8-bit quantization.  \n",
    "\n",
    "Herein, for demonstration purposes, we take only the 4 images that we downloaded for calibration. In production, this set should be more representative of the production data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oRXVurzh6o5T"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.kill(os.getpid(), 9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DGXjp3K_6x59"
   },
   "outputs": [],
   "source": [
    "\n",
    "from __future__ import absolute_import, division, print_function, unicode_literals\n",
    "import os\n",
    "import time\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.python.compiler.tensorrt import trt_convert as trt\n",
    "from tensorflow.python.saved_model import tag_constants\n",
    "from tensorflow.keras.applications.resnet50 import ResNet50\n",
    "from tensorflow.keras.preprocessing import image\n",
    "from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "01lqtLJzvwSS",
    "outputId": "cca3c2d1-a697-4a25-c597-d16619da56d1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "batched_input shape:  (8, 224, 224, 3)\n"
     ]
    }
   ],
   "source": [
    "batch_size = 8\n",
    "batched_input = np.zeros((batch_size, 224, 224, 3), dtype=np.float32)\n",
    "\n",
    "for i in range(batch_size):\n",
    "  img_path = './data/img%d.JPG' % (i % 4)\n",
    "  img = image.load_img(img_path, target_size=(224, 224))\n",
    "  x = image.img_to_array(img)\n",
    "  x = np.expand_dims(x, axis=0)\n",
    "  x = preprocess_input(x)\n",
    "  batched_input[i, :] = x\n",
    "batched_input = tf.constant(batched_input)\n",
    "print('batched_input shape: ', batched_input.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 201
    },
    "colab_type": "code",
    "id": "iM8DshiYkVQe",
    "outputId": "d3e88347-e060-4fd2-de2b-79cb38fc8442",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converting to TF-TRT INT8...\n",
      "INFO:tensorflow:Linked TensorRT version: (7, 1, 3)\n",
      "INFO:tensorflow:Loaded TensorRT version: (7, 1, 3)\n",
      "INFO:tensorflow:Assets written to: resnet50_saved_model_TFTRT_INT8/assets\n",
      "Done Converting to TF-TRT INT8\n"
     ]
    }
   ],
   "source": [
    "print('Converting to TF-TRT INT8...')\n",
    "conversion_params = trt.DEFAULT_TRT_CONVERSION_PARAMS._replace(\n",
    "    precision_mode=trt.TrtPrecisionMode.INT8, \n",
    "    max_workspace_size_bytes=8000000000, \n",
    "    use_calibration=True)\n",
    "converter = trt.TrtGraphConverterV2(\n",
    "    input_saved_model_dir='resnet50_saved_model', \n",
    "    conversion_params=conversion_params)\n",
    "\n",
    "def calibration_input_fn():\n",
    "    yield (batched_input, )\n",
    "converter.convert(calibration_input_fn=calibration_input_fn)\n",
    "\n",
    "converter.save(output_saved_model_dir='resnet50_saved_model_TFTRT_INT8')\n",
    "print('Done Converting to TF-TRT INT8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gu0CSNGVvwSY"
   },
   "outputs": [],
   "source": [
    "def predict_tftrt(input_saved_model):\n",
    "    \"\"\"Runs prediction on a single image and shows the result.\n",
    "    input_saved_model (string): Name of the input model stored in the current dir\n",
    "    \"\"\"\n",
    "    img_path = './data/img0.JPG'  # Siberian_husky\n",
    "    img = image.load_img(img_path, target_size=(224, 224))\n",
    "    x = image.img_to_array(img)\n",
    "    x = np.expand_dims(x, axis=0)\n",
    "    x = preprocess_input(x)\n",
    "    x = tf.constant(x)\n",
    "    \n",
    "    saved_model_loaded = tf.saved_model.load(input_saved_model, tags=[tag_constants.SERVING])\n",
    "    signature_keys = list(saved_model_loaded.signatures.keys())\n",
    "    print(signature_keys)\n",
    "\n",
    "    infer = saved_model_loaded.signatures['serving_default']\n",
    "    print(infer.structured_outputs)\n",
    "\n",
    "    labeling = infer(x)\n",
    "    preds = labeling['predictions'].numpy()\n",
    "    print('{} - Predicted: {}'.format(img_path, decode_predictions(preds, top=3)[0]))\n",
    "    plt.subplot(2,2,1)\n",
    "    plt.imshow(img);\n",
    "    plt.axis('off');\n",
    "    plt.title(decode_predictions(preds, top=3)[0][0][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 238
    },
    "colab_type": "code",
    "id": "DSKfqPDGEv7U",
    "outputId": "028545e1-26e5-4fb6-930f-7b10af2c82fe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['serving_default']\n",
      "{'predictions': TensorSpec(shape=<unknown>, dtype=tf.float32, name='predictions')}\n",
      "./data/img0.JPG - Predicted: [('n02110185', 'Siberian_husky', 0.56689453), ('n02109961', 'Eskimo_dog', 0.40820312), ('n02110063', 'malamute', 0.020324707)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict_tftrt('resnet50_saved_model_TFTRT_INT8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DMVOHSo_vwSd"
   },
   "outputs": [],
   "source": [
    "def benchmark_tftrt(input_saved_model):\n",
    "    saved_model_loaded = tf.saved_model.load(input_saved_model, tags=[tag_constants.SERVING])\n",
    "    infer = saved_model_loaded.signatures['serving_default']\n",
    "\n",
    "    N_warmup_run = 50\n",
    "    N_run = 1000\n",
    "    elapsed_time = []\n",
    "\n",
    "    for i in range(N_warmup_run):\n",
    "      labeling = infer(batched_input)\n",
    "\n",
    "    for i in range(N_run):\n",
    "      start_time = time.time()\n",
    "      labeling = infer(batched_input)\n",
    "      #prob = labeling['probs'].numpy()\n",
    "      end_time = time.time()\n",
    "      elapsed_time = np.append(elapsed_time, end_time - start_time)\n",
    "      if i % 50 == 0:\n",
    "        print('Step {}: {:4.1f}ms'.format(i, (elapsed_time[-50:].mean()) * 1000))\n",
    "\n",
    "    print('Throughput: {:.0f} images/s'.format(N_run * batch_size / elapsed_time.sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2mM9D3BTEzQS"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Unresolved object in checkpoint: (root).trt_engine_resources.TRTEngineOp_0_0._serialized_trt_resource_filename\n",
      "WARNING:tensorflow:A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details.\n",
      "Step 0:  3.3ms\n",
      "Step 50:  2.7ms\n",
      "Step 100:  2.6ms\n",
      "Step 150:  2.6ms\n",
      "Step 200:  2.6ms\n",
      "Step 250:  2.6ms\n",
      "Step 300:  2.6ms\n",
      "Step 350:  2.6ms\n",
      "Step 400:  2.6ms\n",
      "Step 450:  2.6ms\n",
      "Step 500:  2.6ms\n",
      "Step 550:  2.6ms\n",
      "Step 600:  2.6ms\n",
      "Step 650:  2.6ms\n",
      "Step 700:  2.6ms\n",
      "Step 750:  2.6ms\n",
      "Step 800:  2.6ms\n",
      "Step 850:  2.6ms\n",
      "Step 900:  2.7ms\n",
      "Step 950:  2.7ms\n",
      "Throughput: 3049 images/s\n"
     ]
    }
   ],
   "source": [
    "benchmark_tftrt('resnet50_saved_model_TFTRT_INT8')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "I13snJ9VkVQh"
   },
   "source": [
    "## Conclusion\n",
    "In this notebook, we have demonstrated the process of creating TF-TRT FP32, FP16 and INT8 inference models from an original Keras FP32 model, as well as verify their speed and accuracy. \n",
    "\n",
    "### What's next\n",
    "Try TF-TRT on your own model and data, and experience the simplicity and speed up it offers."
   ]
  }
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